An interactive computer system evaluates and diagnoses based
on cytologic features derived directly from a digital scan of
fineneedle aspirates (FNA) slides.  A consecutive series of 569
patients provided the data to develop the system and an additional 54
consecutive, new patients provided samples to test the system.
	The projected prospective accuracy of the system estimated by
tenfold cross validation was 97%.  The actual accuracy on 54 new
samples (36 benign, 1 atypia, and 17 malignant) was 100%.
	Digital image analysis coupled with machine learning
techniques will improve diagnostic accuracy of breast fine needle
aspirates.